# Boosting方法中的特征重要性

Posted by c cm on July 15, 2017

## XGBOOST

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   def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
'weight' - the number of times a feature is used to split the data across all trees.
'gain' - the average gain of the feature when it is used in trees
'cover' - the average coverage of the feature when it is used in trees
Parameters
----------
fmap: str (optional)
The name of feature map file
"""

• weight 在tree中用到的次数计数
• gain 在tree中用到时的gain之和/在tree中用到的次数计数

## LightGBM

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    def feature_importance(self, importance_type='split'):
"""
Get feature importances

Parameters
----------
importance_type : str, default "split"
How the importance is calculated: "split" or "gain"
"split" is the number of times a feature is used in a model
"gain" is the total gain of splits which use the feature

Returns
-------
result : array
Array of feature importances.
"""


## CATBoosting

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#### 1. Regular feature importance

• $feature_total_importance_j$ is the individual feature importance of the j-th feature.
• $average_feature_importance$ is the average feature importance of the j-th feature in the i-th combinational feature.